Exploring the Use of Activity Trackers to Support Physical Activity and Reduce Sedentary Behavior in Adults Diagnosed With Type 2 Diabetes: Qualitative Interview Study Using the RE-AIM Framework
Bibliographic record
Abstract
Background: The prevalence of type 2 diabetes in adults worldwide is increasing. Low levels of physical activity and sedentary behavior are major risk factors for developing the disease. Physical activity interventions incorporating activity trackers can reduce blood glucose levels in adults diagnosed with type 2 diabetes. The My Diabetes My Way website is a support and educational platform for people diagnosed with diabetes and health care professionals. Users of the My Diabetes My Way website can upload their Fitbit (Google Inc) activity data into the system but this is not presently being analyzed and used routinely within clinical care. Developers of the My Diabetes My Way system are planning to allow different makes of activity trackers to be integrated with the platform. Objective: This qualitative study aimed to explore (through the RE-AIM [reach, effectiveness, adoption, implementation, and maintenance] framework) views from adults diagnosed with type 2 diabetes and health care professionals on the integration of activity trackers into type 2 diabetes care. Methods: Overall, 12 adults diagnosed with type 2 diabetes and 9 health care professionals (4 general practitioners, 1 consultant, 2 diabetes nurses, 1 practice nurse, and 1 physical activity advisor) were recruited through social media and professional contacts. Semistructured one-to-one interviews were conducted. Abductive thematic analysis was undertaken, and main themes and subthemes were identified. The RE-AIM framework was used to evaluate the themes with respect to the wider use of activity trackers and the My Diabetes My Way platform within type 2 diabetes clinical care. Results: Overall, 6 main themes (awareness, access, cost, promotion, support, and technology and data) and 20 subthemes were identified. Evaluation using the 5 RE-AIM dimensions found that reach could be improved by raising awareness of the My Diabetes My Way platform and the ability to upload activity tracker data into the system. Effectiveness could be improved by implementing appropriate personalized measures of health benefits and providing appropriate support for patients and health care staff. Adoption could be improved by better promotion of the intervention among stakeholders and the development of joint procedures. Implementation could be improved through the development of an agreed protocol, staff training, and introducing measurements of costs. Maintenance could be improved by supporting all patients for long-term engagement and measuring improvements to patients' health. Conclusions: Through this study, we identified how the reach, effectiveness, adoption, implementation, and maintenance of integrating activity trackers into adult type 2 diabetes care could be improved.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".